考虑驾驶风格的闭环反馈车速引导方法研究

信号交叉口的车速控制不当会降低车辆的燃油经济性甚至引起追尾碰撞事故,车路协同环境下的车速引导系统可以有效提高信号交叉口处的通行效率和燃油经济性.现有车速引导研究大多忽略了驾驶员风格的差异性,将导致驾驶员无法准确跟踪引导速度.针对该问题,建立考虑驾驶风格的闭环反馈车速引导模型.首先,分析不同风格驾驶员车辆最大纵向加速度的...     

城市中心体系与轨道交通耦合演化规律

随着中国大城市轨道交通规划建设速度的加快,亟须探索城市中心体系与轨道交通耦合演化规律。在分析城市中心体系和轨道交通耦合空间基础、系统互动演化过程及演化政策影响的基础上,对单系统演化和耦合演化模型进行推演,建立城市中心体系耦合演化模型,揭示城市中心体系与轨道交通耦合演化规律。最后,提出基于土地与交通协调的双系统耦合规划技术路线,以及不同发展阶段的城市中心体系和轨道交通耦合规划策略。     

城市停车诱导信息系统理论动态研究

本文按照配备停车诱导信息系统条件下停车选择模型、显示优化技术,设计思想和管理规定三方面阐述了国外城市停车诱导信息系统理论研究的既有成果,从结构组成探讨,服务功能和显示技术、相关模型三方面概括了国内对谊系统研究的基本情况。国外在谊领域研究实践的广度和深度都领先于国内．总结了目前理论研究存在的不足．最后提出了停车小区和停车片区的新概念,并对未来城市停车诱导信息系统理论研究动态进行展望．     

基于短时交通流预测的广域动态交通路径诱导方法

为提升车辆通行效率,以预测型诱导策略为基础,以排队长度作为交通诱导的约束条件,利用小波神经网络短时交通量预测预知路段堵死事件发生路段,通过广域诱导时空边界条件对事件路段进行节点分级和诱导周期长度界定,进而建立广域诱导模型;对事件区域路网进行分区,进一步确定该模型诱导起点位置,引入基于路径尺度的Logit 路径选择模型作为诱导路径选择方法,通过流量迭代分配方法实现路网负载均衡. 通过实例验证,该诱导方法能有效地缓解道路交通拥堵,提高路网通行效率.     

城市停车诱导系统设计研究

分析了该系统的目的、基本功能,提出了系统框架设计和功能模块设计内容 从管理层次和技术面层次,提出了系统总体设计框架,最后结合我国大城市停车现状和研究的交通拥挤状况,提出了停车诱导系统的发展方向。     

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns

Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions.     

Multi-day activity-travel pattern sampling based on single-day data

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.     

动态交通流诱导模型在交通出行服务中的应用研究

本文从浙江交通智能交通建设应用需求出发,分析了动态交通流诱导模型在浙江交通出行服务中的实际应用,提出了浙江交通高速公路、内河航道实时交通状况分析的方案。该方案包括了基于多个Agent诱导代理系统的诱导模型,交通流诱导系统框架等自主技术研究成果。同时,本文给出了在浙江交通出行服务实际应用中,结合GPS定位技术、GIS地理信息技术、GSM/CDMA无线通信技术,实现了浙江交通高速公路、内河航道实时交通状况的分析和播报的应用场景。     

An investigation of the reliability of real-time information for route choice decisions in a congested traffic system

This paper investigates the reliability of information on prevailing trip times on the links of a network as a basis for route choice decisions by individual drivers. It considers a type of information strategy in which no attempt is made by some central controller or coordinating entity to predict what the travel times on each link would be by the time it is reached by a driver that is presently at a given location. A specially modified model combining traffic simulation and path assignment capabilities is used to analyze the reliability of the real-time information supplied to the drivers. This is accomplished by comparing the supplied travel times (at the link and path levels) to the actual trip times experienced in the network after the information has been given. In addition, the quality of the decisions made by drivers on the basis of this information (under alternative path switching rules) is evaluated ex-post by comparing the actually experienced travel time (given the decision made) to the time that the driver would have experienced without the real-time information. Results of a series of simulation experiments under recurrent congestion conditions are discussed, illustrating the interactions between information reliability and user response.